Hospitalizations for Cardiovascular Diseases Sensitive to Primary Health Care in Paraná State, Brazil: A Bayesian Spatiotemporal Model.

PURPOSE: To analyze spatiotemporal trends in hospitalizations for cardiovascular diseases (CVD) sensitive to primary health care (PHC) among individuals aged 50-69 years in Paraná State, Brazil, from 2014 to 2019 and investigate correlations between PHC services and the Social Development Index. METHODS: We conducted a cross-sectional ecological study using publicly available secondary data to analyze the municipal incidence of hospitalizations for CVD sensitive to PHC and to estimate the risk of hospitalization for this group of diseases and associated factors using hierarchical Bayesian spatiotemporal modeling with Markov chain Monte Carlo simulation. RESULTS: There was a 5% decrease in the average rate of hospitalizations for PHC-sensitive CVD from 2014 to 2019. Regarding standardized hospitalization rate (SHR) according to population size, we found that no large municipality had an SHR >2. Likewise, a minority of these municipalities had SHR values of 1-2 (33%). However, many small and medium-sized municipalities had SHR values >2 (47% and 48%, respectively). A greater Social Development Index value served as a protective factor against hospitalizations, with a relative risk of 0.957 (95% credible interval, 0.929-0.984). CONCLUSIONS: The annual risk of hospitalization decreased over time; however, small municipalities had the greatest rates of hospitalization, indicating an increase in health inequity. The inverse association between social development and hospitalizations for CVD sensitive to PHC raises questions about intersectionality in health care.

Large-scale genome-wide association study, using historical data, identifies conserved genetic architecture of cyanogenic glucoside content in cassava (Manihot esculenta Crantz) root.

Manihot esculenta (cassava) is a root crop originating from South America that is a major staple in the tropics, including in marginal environments. This study focused on South American and African germplasm and investigated the genetic architecture of hydrogen cyanide (HCN), a major component of root quality. HCN, representing total cyanogenic glucosides, is a plant defense component against herbivory but is also toxic for human consumption. We genotyped 3354 landraces and modern breeding lines originating from 26 Brazilian states and 1389 individuals were phenotypically characterized across multi-year trials for HCN. All plant material was subjected to high-density genotyping using genotyping by sequencing. We performed genome-wide association mapping to characterize the genetic architecture and gene mapping of HCN. Field experiments revealed strong broad- and narrow-sense trait heritability (0.82 and 0.41, respectively). Two major loci were identified, encoding for an ATPase and a MATE protein, and contributing up to 7 and 30% of the HCN concentration in roots, respectively. We developed diagnostic markers for breeding applications, validated trait architecture consistency in African germplasm and investigated further evidence for the domestication of sweet and bitter cassava. Fine genomic characterization revealed: (i) the major role played by vacuolar transporters in regulating HCN content; (ii) the co-domestication of sweet and bitter cassava major alleles are dependent upon geographical zone; and (iii) the major loci allele for high HCN in M. esculenta Crantz seems to originate from its ancestor, M. esculenta subsp. flabellifolia. Taken together, these findings expand our insights into cyanogenic glucosides in cassava roots and its glycosylated derivatives in plants.

Impact of socioeconomic factors and health determinants on preterm birth in Brazil: a register-based study.

BACKGROUND: More than 15 million children are born preterm annually. While preterm survival rates have increased in high-income countries. Low- and middle-income countries, like Brazil, continue to battle high neonatal mortality rates due to a lack of adequate postnatal care. Globally, neonatal mortality is higher for preterm infants compared to those born at term. Our study aims to map and analyze the spatial, socioeconomic, and health coverage determinants related to preterm birth in Brazil in order to understand how spatial variations in demographics and access to primary care may affect preterm birth occurrences.  METHODS: Using publicly available national-level data from the Brazilian health system for 2008-2017, we conducted an ecological study to visualize the spatial distributions of preterm birth along with socioeconomic status, the structure of health services, and primary care work process, each consisting of multiple variables reduced via principal component analysis. Regression models were created to determine predictive effects of numeric and spatial variation of these scores on preterm birth rates. RESULTS: In Brazil, preterm birth rates increased from 2008-2017, with small and rural municipalities frequently exhibiting higher rates than urban areas. Scores in socioeconomic status and work process were significant predictors of preterm birth rates, without taking into account spatial adjustment, with more positive scores in socioeconomic status predicting higher preterm birth rates (coefficient 0.001145) and higher scores in work process predicting lower preterm birth rates (coefficient -0.002416). Geographically weighted regression showed socioeconomic status to be a more significant predictor in the North, with the work process indicators being most significant in the Northeast. CONCLUSIONS: Results support that primary care work process indicators are more significant in estimating preterm birth rates than physical structures available for care. These results emphasize the importance of ensuring the presence of the minimum human resources needed, especially in the most deprived areas of Brazil. The association between social determinants of health and preterm birth rates raises questions regarding the importance of policies dedicated to foster equity in the accessibility of healthcare services, and improve income as protective proxies for preterm birth.

Comprehensive genotyping of a Brazilian cassava (Manihot esculenta Crantz) germplasm bank: insights into diversification and domestication.

KEY MESSAGE: Brazilian cassava diversity was characterized through population genetics and clustering approaches, highlighting contrasted genetic groups and spatial genetic differentiation. Cassava (Manihot esculenta Crantz) is a major staple root crop of the tropics, originating from the Amazonian region. In this study, 3354 cassava landraces and modern breeding lines from the Embrapa Cassava Germplasm Bank (CGB) were characterized. All individuals were subjected to genotyping-by-sequencing (GBS), identifying 27,045 single-nucleotide polymorphisms (SNPs). Identity-by-state and population structure analyses revealed a unique set of 1536 individuals and 10 distinct genetic groups with heterogeneous linkage disequilibrium (LD). On this basis, a density of 1300-4700 SNP markers were selected for large-effect quantitative trait loci (QTL) detection. Identified genetic groups were further characterized for population genetics parameters including minor allele frequency (MAF), observed heterozygosity [Formula: see text], effective population size estimate [Formula: see text]) and polymorphism information content (PIC). Selection footprints and introgressions of M. glaziovii were detected. Spatial population structure analysis revealed five ancestral populations related to distinct Brazilian ecoregions. Estimation of historical relationships among identified populations suggests an early population split from Amazonian to Atlantic forest and Caatinga ecoregions and active gene flows. This study provides a thorough genetic characterization of ex situ germplasm resources from cassava's center of origin, South America, with results shedding light on Brazilian cassava characteristics and its biogeographical landscape. These findings support and facilitate the use of genetic resources in modern breeding programs including implementation of association mapping and genomic selection strategies.

Helicopter Transportation of Brazilian Trauma Patients: A Comparison of Times to Care.

OBJECTIVE: The purpose of this study was to analyze helicopter emergency medical service (HEMS) transport with secondary land ambulance transfer, comparing landings performed inside and outside the hospital complex to the emergency department. METHODS: This was a cross-sectional observational study of HEMS transports of trauma patients between 2016 and 2018 in southern Brazil. Patients were attended by the HEMS team at the trauma site or stabilized in hospitals nearby and subsequently referred to trauma centers. In this region, no trauma centers have their own helipads so helicopters land in remote areas close to the hospital, which may be inside or outside the hospital complex. Both landings require ground emergency medical service transport, with off-site landings necessitating ground emergency medical service transport via public access roads to reach the hospital. Data were analyzed using descriptive statistics, and on-site and off-site transport times were compared using a t-test for independent samples. RESULTS: Of 176 transports, 28.5% resulted in on-site landings, whereas 71.5% occurred off-site. The ground transport time when the landing zone was off-site was 5 minutes longer than on-site (P < .001). CONCLUSION: Off-site landings result in longer transports to the emergency room. The construction of helipads in trauma centers can reduce transport time, in addition to reducing the costs and sequelae of trauma.

Quantifying the pediatric surgical need in Uganda: results of a nationwide cross-sectional, household survey.

PURPOSE: Little is known about the prevalence of pediatric surgical conditions in low- and middle-income countries. Many children never seek medical care, thus the true prevalence of surgical conditions in children in Uganda is unknown. The objective of this study was to determine the prevalence of surgical conditions in children in Uganda. METHODS: Using the Surgeons OverSeas Assessment of Surgical Need (SOSAS) survey, we enumerated 4248 individuals in 2315 households in 105 randomly selected clusters throughout Uganda. Children aged 0-18 were included if randomly selected from the household; for those who could not answer for themselves, parents served as surrogates. RESULTS: Of 2176 children surveyed, 160 (7.4 %) reported a currently untreated surgical condition. Lifetime prevalence of surgical conditions was 14.0 % (305/2176). The predominant cause of surgical conditions was trauma (48.4 %), followed by wounds (19.7 %), acquired deformities (16.2 %), and burns (12.5 %). Of 90 pediatric household deaths, 31.1 % were associated with a surgically treatable proximate cause of death (28/90 deaths). CONCLUSION: Although some trauma-related surgical burden among children can be adequately addressed at district hospitals, the need for diagnostics, human resources, and curative services for more severe trauma cases, congenital deformities, and masses outweighs the current capacity of hospitals and trained pediatric surgeons in Uganda.

Mortality in Women with Coronary Artery Disease in Paraná State, Brazil: A Bayesian Spatiotemporal Analysis.

Background: Mortality resulting from coronary artery disease (CAD) among women is a complex issue influenced by many factors that encompass not only biological distinctions but also sociocultural, economic, and healthcare-related components. Understanding these factors is crucial to enhance healthcare provisions. Therefore, this study seeks to identify the social and clinical variables related to the risk of mortality caused by CAD in women aged 50 to 79 years old in Paraná state, Brazil, between 2010 and 2019. Methods: This is an ecological study based on secondary data sourced from E-Gestor, IPARDES, and DATASUS. We developed a model that integrates both raw and standardized coronary artery disease (CAD) mortality rates, along with sociodemographic and healthcare service variables. We employed Bayesian spatiotemporal analysis with Markov Chain Monte Carlo simulations to assess the relative risk of CAD mortality, focusing specifically on women across the state of Paraná. Results: A total of 14,603 deaths from CAD occurred between 2010 and 2019. Overall, temporal analysis indicates that the risk of CAD mortality decreased by around 22.6% between 2010 (RR of 1.06) and 2019 (RR of 0.82). This decline was most prominent after 2014. The exercise stress testing rate, accessibility of cardiology centers, and IPARDES municipal performance index contributed to the reduction of CAD mortality by approximately 4%, 8%, and 34%, respectively. However, locally, regions in the Central-West, Central-South, Central-East, and Southern regions of the Central-North parts of the state exhibited risks higher-than-expected. Conclusion: In the last decade, CAD-related deaths among women in Paraná state decreased. This was influenced by more exercise stress testing, better access to cardiology centers, improved municipal performance index. Yet, elevated risks of deaths persist in certain regions due to medical disparities and varying municipal development. Therefore, prioritizing strategies to enhance women's access to cardiovascular healthcare in less developed regions is crucial.

Bayesian Modeling and Estimation of Spatial Risk for Hospitalization and Mortality from Ischemic Heart Disease in Paraná, Brazil.

Objective: Despite significant advancements in understanding risk factors and treatment strategies, ischemic heart disease (IHD) remains the leading cause of mortality worldwide, particularly within specific regions in Brazil, where the disease is a burden. Therefore, the aim of this study was to estimate the risk of hospitalization and mortality from IHD in the state of Paraná (Brazil), using spatial analysis to identify areas with higher risk based on socioeconomic, demographic and health variables. Methods: This is an ecological study based on secondary and retrospective IHD hospitalization and mortality data obtained from the Brazilian Hospitalization and Mortality Information Systems during the 2010-2021 period. Data were analyzed for 399 municipalities and 22 health regions in the state of Paraná. To assess the spatial patterns of the disease and identify relative risk (RR) areas, we constructed a risk model by Bayesian inference using the R-INLA and SpatialEpi packages in R software. Results: A total of 333,229 hospitalizations and 73,221 deaths occurred in the analyzed period, and elevated RR of hospitalization (RR = 27.412, CI 21.801; 34.466) and mortality (RR = 15.673, CI 2.148; 114.319) from IHD occurred in small-sized municipalities. In addition, medium-sized municipalities also presented elevated RR of hospitalization (RR = 6.533, CI 1.748; 2.006) and mortality (RR = 6.092, CI 1.451; 2.163) from IHD. Hospitalization and mortality rates were higher in white men aged 40-59 years. A negative association was found between Municipal Performance Index (IPDM) and IHD hospitalization and mortality. Conclusion: Areas with increased risk of hospitalization and mortality from IHD were found in small and medium-sized municipalities in the state of Paraná, Brazil. These results suggest a deficit in health care attention for IHD cases in these areas, potentially due to a low distribution of health care resources.

Confronting mobile phone signal coverage and helicopter emergency medical service travel time: A geospatial analysis in the northwest macro-region of Paraná State, Brazil.

OBJECTIVE: The aim of this study was to conduct a detailed geospatial analysis of mobile phone signal coverage in the northwest macro-region of Paraná State, Brazil, seeking to identify areas where limitations in coverage may be related to lengthy travel times of the helicopter emergency medical service (HEMS) for the assistance of victims of road traffic injuries (RTIs). METHODS: An observational study was conducted to examine mobile phone signal coverage and HEMS travel times from 2017 to 2021. HEMS travel times were categorized into four groups: T1 (0-15 min), T2 (16-30 min), T3 (31-45 min), and T4 (over 45 min). Empirical Bayesian Kriging was used to map areas with low mobile signal coverage. The Kruskal-Wallis test and Dwass-Steel-Critchlow-Fligner comparative analyses were performed to explore how mobile signal coverage relates to HEMS travel times to RTI locations. RESULTS: There were 470 occurrences of RTIs attended by HEMS, of which 108 (23%) resulted in on-site fatalities. Among these deaths, 47 (26.85%) occurred in areas with low mobile phone signal coverage ("shadow areas"). Low mobile phone signal coverage identified at 175 (37.24%) RTIs locations, was unevenly distributed across the macro-region. The lowest medians of mobile signal quality were predominantly found in areas with HEMS travel times exceeding 30 min, corresponding to signal strength values of -98.44 (T3) and -100.75 (T4) dBm. This scenario represents a challenge for effective communication to activate HEMS. In the multiple comparison analysis among travel time groups, significant differences were observed between T1 and T2 (p < 0.001), T1 and T3 (p < 0.001), T1 and T4 (p < 0.001), and T2 and T3 (p < 0.001), indicating a potential association between lower mobile phone signal coverage and longer HEMS travel times. CONCLUSION: It can be concluded that poor mobile phone signals in remote areas can hinder HEMS activation, potentially delaying the start of treatment for RTIs. Identification of the shadow areas can help communication and health managers in designing and implementing the necessary changes to improve mobile phone signal coverage and consequently reduce delays in the initial response to RTIs.

Applicability of machine learning algorithm to predict the therapeutic intervention success in Brazilian smokers.

Smoking cessation is an important public health policy worldwide. However, as far as we know, there is a lack of screening of variables related to the success of therapeutic intervention (STI) in Brazilian smokers by machine learning (ML) algorithms. To address this gap in the literature, we evaluated the ability of eight ML algorithms to correctly predict the STI in Brazilian smokers who were treated at a smoking cessation program in Brazil between 2006 and 2017. The dataset was composed of 12 variables and the efficacies of the algorithms were measured by accuracy, sensitivity, specificity, positive predictive value (PPV) and area under the receiver operating characteristic curve. We plotted a decision tree flowchart and also measured the odds ratio (OR) between each independent variable and the outcome, and the importance of the variable for the best model based on PPV. The mean global values for the metrics described above were, respectively, 0.675±0.028, 0.803±0.078, 0.485±0.146, 0.705±0.035 and 0.680±0.033. Supporting vector machines performed the best algorithm with a PPV of 0.726±0.031. Smoking cessation drug use was the roof of decision tree with OR of 4.42 and importance of variable of 100.00. Increase in the number of relapses also promoted a positive outcome, while higher consumption of cigarettes resulted in the opposite. In summary, the best model predicted 72.6% of positive outcomes correctly. Smoking cessation drug use and higher number of relapses contributed to quit smoking, while higher consumption of cigarettes showed the opposite effect. There are important strategies to reduce the number of smokers and increase STI by increasing services and drug treatment for smokers.

Designing, development and validation of an app to reduce the response time of the emergency medical services.

INTRODUCTION: Delays in prehospital care attributable to the call-taking process can often be traced back to miscommunication, including uncertainty around the call location. Geolocation applications have the potential to streamline the call-taking process by accurately identifying the caller's location. OBJECTIVE: To develop and validate an application to geolocate emergency calls and compare the response time of calls made via the application with those of conventional calls made to the Brazilian Medical Emergency System (Serviço de Atendimento Médico de Urgência-SAMU). METHODS: This study was conducted in two stages. First, a geolocating application for SAMU emergency calls (CHAMU192) was developed using a mixed methods approach based on design thinking and subsequently validated using the System Usability Scale (SUS). In the second stage, sending time of the geolocation information of the app was compared with the time taken to process information through conventional calls. For this, a hypothetical case control study was conducted with SAMU in the Maringá, Paraná, Brazil. A control group of 350 audio recordings of emergency calls from 2019 was compared to a set of test calls made through the CHAMU192 app. The CHAMU192 group consisted of 201 test calls in Maringá. In test calls, the location was obtained by GPS and sent to the SAMU communication system. Comparative analysis between groups was performed using the Mann-Whitney test. RESULTS: CHAMU192 had a SUS score of 90, corresponding to a "best imaginable" usability rating. The control group had a median response time of 35.67 seconds (26.00-48.12). The response time of the CHAMU192 group was 0.20 (0.15-0.24). CONCLUSION: The use of the CHAMU192 app by emergency medical services could significantly reduce response time. The results demonstrate the potential of app improving the quality and patient outcomes related to the prehospital emergency care services.

Mortality of Young Women due to Breast Cancer in Low, Middle and High-Income Countries: Systematic Literature Review and Meta-Analysis.

OBJECTIVE: To identify the difference in breast cancer mortality rates among young women according to countries' economic classification. METHODS: A systematic literature review included retrospective studies on breast cancer mortality rates in women aged 20 to 49 years. Databases used were PubMed, Web of Science, Scopus, and Virtual Health Library, with articles selected in English, Portuguese, and Spanish. The study selection and analysis were conducted by two pairs of researchers. Data from 54 countries were extracted, including 39 high-income, 12 upper-middle-income, and 3 lower-middle-income countries. A meta-analysis was performed with the quantitative data from two studies. RESULTS: Six articles met the inclusion criteria. Four were analyzed descriptively due to data diversity, and two were included in the meta-analysis. The pooled mortality rate for high-income countries was 10.2 per 100,000 women (95% CI: 9.8-10.6), while for upper-middle-income countries, it was 15.5 per 100,000 women (95% CI: 14.9-16.1). Lower-middle-income countries had a pooled mortality rate of 20.3 per 100,000 women (95% CI: 19.5-21.1). The decrease in mortality rates in high-income countries was statistically significant (p<0.05). CONCLUSION: Mortality rates for breast cancer among young women have decreased significantly in high-income countries but have increased in lower-income countries. This disparity underscores the impact of insufficient investment in preventive measures, health promotion, early diagnosis, and treatment on young women's mortality in lower-income countries.

Near-infrared spectroscopy for early selection of waxy cassava clones via seed analysis.

Cassava (Manihot esculenta Crantz) starch consists of amylopectin and amylose, with its properties determined by the proportion of these two polymers. Waxy starches contain at least 95% amylopectin. In the food industry, waxy starches are advantageous, with pastes that are more stable towards retrogradation, while high-amylose starches are used as resistant starches. This study aimed to associate near-infrared spectrophotometry (NIRS) spectra with the waxy phenotype in cassava seeds and develop an accurate classification model for indirect selection of plants. A total of 1127 F2 seeds were obtained from controlled crosses performed between 77 F1 genotypes (wild-type, Wx_). Seeds were individually identified, and spectral data were obtained via NIRS using a benchtop NIRFlex N-500 and a portable SCiO device spectrometer. Four classification models were assessed for waxy cassava genotype identification: k-nearest neighbor algorithm (KNN), C5.0 decision tree (CDT), parallel random forest (parRF), and eXtreme Gradient Boosting (XGB). Spectral data were divided between a training set (80%) and a testing set (20%). The accuracy, based on NIRFlex N-500 spectral data, ranged from 0.86 (parRF) to 0.92 (XGB). The Kappa index displayed a similar trend as the accuracy, considering the lowest value for the parRF method (0.39) and the highest value for XGB (0.71). For the SCiO device, the accuracy (0.88-0.89) was similar among the four models evaluated. However, the Kappa index was lower than that of the NIRFlex N-500, and this index ranged from 0 (parRF) to 0.16 (KNN and CDT). Therefore, despite the high accuracy these last models are incapable of correctly classifying waxy and non-waxy clones based on the SCiO device spectra. A confusion matrix was performed to demonstrate the classification model results in the testing set. For both NIRS, the models were efficient in classifying non-waxy clones, with values ranging from 96-100%. However, the NIRS differed in the potential to predict waxy genotype class. For the NIRFlex N-500, the percentage ranged from 30% (parRF) to 70% (XGB). In general, the models tended to classify waxy genotypes as non-waxy, mainly SCiO. Therefore, the use of NIRS can perform early selection of cassava seeds with a waxy phenotype.

Development of cassava core collections based on morphological and agronomic traits and SNPS markers.

Cassava (Manihot esculenta Crantz) holds significant importance as one of the world's key starchy crop species. This study aimed to develop core collections by utilizing both phenotypic data (15 quantitative and 33 qualitative descriptors) and genotypic data (20,023 single-nucleotide polymorphisms) obtained from 1,486 cassava accessions. Six core collections were derived through two optimization strategies based on genetic distances: Average accession-to-nearest-entry and Average entry-to-nearest-entry, along with combinations of phenotypic and genotypic data. The quality of the core collections was evaluated by assessing genetic parameters such as genetic diversity Shannon-Weaver Index, inbreeding (Fis), observed (Ho), and expected (Hs) heterozygosity. While the selection of accessions varied among the six core collections, a seventh collection (consolidated collection) was developed, comprising accessions selected by at least two core collections. Most collections exhibited genetic parameters similar to the complete collection, except for those developed by the Average accession-to-nearest-entry algorithm. However, the variations in the maximum and minimum values of Ho, Hs, and Fis parameters closely resembled the complete collection. The consolidated collection and the collection constructed using genotypic data and the Average entry-to-nearest-entry algorithm (GenEN) retained the highest number of alleles (>97%). Although the differences were not statistically significant (above 5%), the consolidated collection demonstrated a distribution profile and mean trait values most similar to the complete collection, with a few exceptions. The Shannon-Weaver Index of qualitative traits exhibited variations exceeding ±10% when compared to the complete collection. Principal component analysis revealed that the consolidated collection selected cassava accessions with a more uniform dispersion in all four quadrants compared to the other core collections. These findings highlight the development of optimized and valuable core collections for efficient breeding programs and genomic association studies.

The Development of Thematic Core Collections in Cassava Based on Yield, Disease Resistance, and Root Quality Traits.

Thematic collections (TCs), which are composed of genotypes with superior agronomic traits and reduced size, offer valuable opportunities for parental selection in plant breeding programs. Three TCs were created to focus on crucial attributes: root yield (CC_Yield), pest and disease resistance (CC_Disease), and root quality traits (CC_Root_quality). The genotypes were ranked using the best linear unbiased predictors (BLUP) method, and a truncated selection was implemented for each collection based on specific traits. The TCs exhibited minimal overlap, with each collection comprising 72 genotypes (CC_Disease), 63 genotypes (CC_Root_quality), and 64 genotypes (CC_Yield), representing 4%, 3.5%, and 3.5% of the total individuals in the entire collection, respectively. The Shannon-Weaver Diversity Index values generally varied but remained below 10% when compared to the entire collection. Most TCs exhibited observed heterozygosity, genetic diversity, and the inbreeding coefficient that closely resembled those of the entire collection, effectively retaining 90.76%, 88.10%, and 88.99% of the alleles present in the entire collection (CC_Disease, CC_Root_quality, and CC_Disease, respectively). A PCA of molecular and agro-morphological data revealed well-distributed and dispersed genotypes, while a discriminant analysis of principal components (DAPC) displayed a high discrimination capacity among the accessions within each collection. The strategies employed in this study hold significant potential for advancing crop improvement efforts.

Correction: Applicability of machine learning technique in the screening of patients with mild traumatic brain injury.

[This corrects the article DOI: 10.1371/journal.pone.0290721.].

Applicability of machine learning technique in the screening of patients with mild traumatic brain injury.

Even though the demand of head computed tomography (CT) in patients with mild traumatic brain injury (TBI) has progressively increased worldwide, only a small number of individuals have intracranial lesions that require neurosurgical intervention. As such, this study aims to evaluate the applicability of a machine learning (ML) technique in the screening of patients with mild TBI in the Regional University Hospital of Maringá, Paraná state, Brazil. This is an observational, descriptive, cross-sectional, and retrospective study using ML technique to develop a protocol that predicts which patients with an initial diagnosis of mild TBI should be recommended for a head CT. Among the tested models, he linear extreme gradient boosting was the best algorithm, with the highest sensitivity (0.70 ± 0.06). Our predictive model can assist in the screening of mild TBI patients, assisting health professionals to manage the resource utilization, and improve the quality and safety of patient care.

Spatial distribution of mortality from colorectal cancer in the southern region of Brazil.

Colorectal cancer (CRC) is the leading cause of death due to cancer worldwide. In Brazil, it is the second most frequent cancer in men and women, with a mortality reaching 9.4% of those diagnosed. The aim of this study was to analyze the spatial heterogeneity of CRC deaths among municipalities in south Brazil, from 2015 to 2019, in different age groups (50-59 years, 60-69 years, 70-79 years, and 80 years old or more) and identify the associated variables. Global Spatial Autocorrelation (Moran's I) and Local Spatial Autocorrelation (LISA) analyses were used to evaluate the spatial correlation between municipalities and CRC mortality. Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) were applied to evaluate global and local correlations between CRC deaths, sociodemographic, and coverage of health care services. For all age groups, our results found areas with high CRC rates surrounded by areas with similarly high rates mainly in the Rio Grande do Sul state. Even as factors associated with CRC mortality varied according to age group, our results suggested that improved access to specialized health centers, the presence of family health strategy teams, and higher rates of colonoscopies are protective factors against colorectal cancer mortality in southern Brazil.

Antivenom access impacts severity of Brazilian snakebite envenoming: A geographic information system analysis.

BACKGROUND: Snakebite envenoming (SBE) is a neglected tropical disease capable of causing both significant disability and death. The burden of SBE is especially high in low- and middle-income countries. The aim of this study was to perform a geospatial analysis evaluating the association of sociodemographics and access to care indicators on moderate and severe cases of SBE in Brazil. METHODS: We conducted an ecological, cross-sectional study of SBE in Brazil from 2014 to 2019 using the open access National System Identification of Notifiable Diseases (SINAN) database. We then collected a set of indicators from the Brazil Census of 2010 and performed a Principal Component Analysis to create variables related to health, economics, occupation, education, infrastructure, and access to care. Next, a descriptive and exploratory spatial analysis was conducted to evaluate the geospatial association of moderate and severe events. These variables related to events were evaluated using Geographically Weighted Poisson Regression. T-values were plotted in choropleth maps and considered statistically significant when values were <-1.96 or >+1.96. RESULTS: We found that the North region had the highest number of SBE cases by population (47.83/100,000), death rates (0.18/100,000), moderate and severe rates (22.96/100,000), and proportion of cases that took more than three hours to reach healthcare assistance (44.11%). The Northeast and Midwest had the next poorest indicators. Life expectancy, young population structure, inequality, electricity, occupation, and more than three hours to reach healthcare were positively associated with greater cases of moderate and severe events, while income, illiteracy, sanitation, and access to care were negatively associated. The remaining indicators showed a positive association in some areas of the country and a negative association in other areas. CONCLUSION: Regional disparities in SBE incidence and rates of poor outcomes exist in Brazil, with the North region disproportionately affected. Multiple indicators were associated with rates of moderate and severe events, such as sociodemographic and health care indicators. Any approach to improving snakebite care must work to ensure the timeliness of antivenom administration.

Built environment influence on the incidence of elderly pedestrian collisions in a medium-large city in southern Brazil: a spatial analysis.

Trauma disproportionately affects vulnerable road users, especially the elderly. We analyzed the spatial distribution of elderly pedestrians struck by vehicles in the urban area of Maringa city, from 2014 to 2018. Hotspots were obtained by kernel density estimation and wavelet analysis. The relationship between spatial relative risks (RR) of elderly run-overs and the built environment was assessed through Qualitative Comparative Analysis (QCA). Incidents were more frequent in the central and southeast regions of the city, where the RR was up to 2.58 times higher. The QCA test found a significant association between elderly pedestrian victims and the presence of traffic lights, medical centers/hospitals, roundabouts and schools. There is an association between higher risk of elderly pedestrians collisions and specific elements of built environments in Maringa, providing fundamental data to help guide public policies to improve urban mobility aimed at protecting vulnerable road users and planning an age-friendly city.